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  <section id="command-line-interface">
<span id="cli"></span><h1>Command-line Interface<a class="headerlink" href="#command-line-interface" title="Permalink to this heading"></a></h1>
<p>The command-line interface provides access to all of COLMAP’s functionality for
automated scripting. Each core functionality is implemented as a command to the
<code class="docutils literal notranslate"><span class="pre">colmap</span></code> executable. Run <code class="docutils literal notranslate"><span class="pre">colmap</span> <span class="pre">-h</span></code> to list the available commands (or
<code class="docutils literal notranslate"><span class="pre">COLMAP.bat</span> <span class="pre">-h</span></code> under Windows). Note that if you run COLMAP from the CMake
build folder, the executable is located at <code class="docutils literal notranslate"><span class="pre">./src/colmap/exe/colmap</span></code>. To start the
graphical user interface, run <code class="docutils literal notranslate"><span class="pre">colmap</span> <span class="pre">gui</span></code>.</p>
<section id="example">
<h2>Example<a class="headerlink" href="#example" title="Permalink to this heading"></a></h2>
<p>Assuming you stored the images of your project in the following structure:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>/path/to/project/...
+── images
│   +── image1.jpg
│   +── image2.jpg
│   +── ...
│   +── imageN.jpg
</pre></div>
</div>
<p>The command for the automatic reconstruction tool would be:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># The project folder must contain a folder &quot;images&quot; with all the images.
$ DATASET_PATH=/path/to/project

$ colmap automatic_reconstructor \
    --workspace_path $DATASET_PATH \
    --image_path $DATASET_PATH/images
</pre></div>
</div>
<p>Note that any command lists all available options using the <code class="docutils literal notranslate"><span class="pre">-h,--help</span></code>
command-line argument. In case you need more control over the individual
parameters of the reconstruction process, you can execute the following sequence
of commands as an alternative to the automatic reconstruction command:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># The project folder must contain a folder &quot;images&quot; with all the images.
$ DATASET_PATH=/path/to/dataset

$ colmap feature_extractor \
   --database_path $DATASET_PATH/database.db \
   --image_path $DATASET_PATH/images

$ colmap exhaustive_matcher \
   --database_path $DATASET_PATH/database.db

$ mkdir $DATASET_PATH/sparse

$ colmap mapper \
    --database_path $DATASET_PATH/database.db \
    --image_path $DATASET_PATH/images \
    --output_path $DATASET_PATH/sparse

$ mkdir $DATASET_PATH/dense

$ colmap image_undistorter \
    --image_path $DATASET_PATH/images \
    --input_path $DATASET_PATH/sparse/0 \
    --output_path $DATASET_PATH/dense \
    --output_type COLMAP \
    --max_image_size 2000

$ colmap patch_match_stereo \
    --workspace_path $DATASET_PATH/dense \
    --workspace_format COLMAP \
    --PatchMatchStereo.geom_consistency true

$ colmap stereo_fusion \
    --workspace_path $DATASET_PATH/dense \
    --workspace_format COLMAP \
    --input_type geometric \
    --output_path $DATASET_PATH/dense/fused.ply

$ colmap poisson_mesher \
    --input_path $DATASET_PATH/dense/fused.ply \
    --output_path $DATASET_PATH/dense/meshed-poisson.ply

$ colmap delaunay_mesher \
    --input_path $DATASET_PATH/dense \
    --output_path $DATASET_PATH/dense/meshed-delaunay.ply
</pre></div>
</div>
<p>If you want to run COLMAP on a computer without an attached display (e.g.,
cluster or cloud service), COLMAP automatically switches to use CUDA if
supported by your system. If no CUDA enabled device is available, you can
manually select to use CPU-based feature extraction and matching by setting the
<code class="docutils literal notranslate"><span class="pre">--SiftExtraction.use_gpu</span> <span class="pre">0</span></code> and <code class="docutils literal notranslate"><span class="pre">--SiftMatching.use_gpu</span> <span class="pre">0</span></code> options.</p>
</section>
<section id="help">
<h2>Help<a class="headerlink" href="#help" title="Permalink to this heading"></a></h2>
<p>The available commands can be listed using the command:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ colmap help

    Usage:
      colmap [command] [options]

    Documentation:
      https://colmap.github.io/

    Example usage:
      colmap help [ -h, --help ]
      colmap gui
      colmap gui -h [ --help ]
      colmap automatic_reconstructor -h [ --help ]
      colmap automatic_reconstructor --image_path IMAGES --workspace_path WORKSPACE
      colmap feature_extractor --image_path IMAGES --database_path DATABASE
      colmap exhaustive_matcher --database_path DATABASE
      colmap mapper --image_path IMAGES --database_path DATABASE --output_path MODEL
      ...

    Available commands:
      help
      gui
      automatic_reconstructor
      bundle_adjuster
      color_extractor
      database_creator
      delaunay_mesher
      exhaustive_matcher
      feature_extractor
      feature_importer
      image_deleter
      image_rectifier
      image_registrator
      image_undistorter
      mapper
      matches_importer
      model_aligner
      model_analyzer
      model_converter
      model_merger
      model_orientation_aligner
      patch_match_stereo
      point_triangulator
      poisson_mesher
      rig_bundle_adjuster
      sequential_matcher
      spatial_matcher
      stereo_fusion
      transitive_matcher
      vocab_tree_builder
      vocab_tree_matcher
      vocab_tree_retriever
</pre></div>
</div>
<p>And each command has a <code class="docutils literal notranslate"><span class="pre">-h,--help</span></code> command-line argument to show the usage and
the available options, e.g.:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ colmap feature_extractor -h

    Options can either be specified via command-line or by defining
    them in a .ini project file passed to `--project_path`.

      -h [ --help ]
      --project_path arg
      --database_path arg
      --image_path arg
      --image_list_path arg
      --ImageReader.camera_model arg (=SIMPLE_RADIAL)
      --ImageReader.single_camera arg (=0)
      --ImageReader.camera_params arg
      --ImageReader.default_focal_length_factor arg (=1.2)
      --SiftExtraction.num_threads arg (=-1)
      --SiftExtraction.use_gpu arg (=1)
      --SiftExtraction.gpu_index arg (=-1)
      --SiftExtraction.max_image_size arg (=3200)
      --SiftExtraction.max_num_features arg (=8192)
      --SiftExtraction.first_octave arg (=-1)
      --SiftExtraction.num_octaves arg (=4)
      --SiftExtraction.octave_resolution arg (=3)
      --SiftExtraction.peak_threshold arg (=0.0066666666666666671)
      --SiftExtraction.edge_threshold arg (=10)
      --SiftExtraction.estimate_affine_shape arg (=0)
      --SiftExtraction.max_num_orientations arg (=2)
      --SiftExtraction.upright arg (=0)
      --SiftExtraction.domain_size_pooling arg (=0)
      --SiftExtraction.dsp_min_scale arg (=0.16666666666666666)
      --SiftExtraction.dsp_max_scale arg (=3)
      --SiftExtraction.dsp_num_scales arg (=10)
</pre></div>
</div>
<p>The available options can either be provided directly from the command-line or
through a <cite>.ini</cite> file provided to <code class="docutils literal notranslate"><span class="pre">--project_path</span></code>.</p>
</section>
<section id="commands">
<h2>Commands<a class="headerlink" href="#commands" title="Permalink to this heading"></a></h2>
<p>The following list briefly documents the functionality of each command, that is
available as <code class="docutils literal notranslate"><span class="pre">colmap</span> <span class="pre">[command]</span></code>:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">gui</span></code>: The graphical user interface, see
<a class="reference internal" href="gui.html#gui"><span class="std std-ref">Graphical User Interface</span></a> for more information.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">automatic_reconstructor</span></code>: Automatically reconstruct sparse and dense model
for a set of input images.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">project_generator</span></code>: Generate project files at different quality settings.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">feature_extractor</span></code>, <code class="docutils literal notranslate"><span class="pre">feature_importer</span></code>: Perform feature extraction or
import features for a set of images.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">exhaustive_matcher</span></code>, <code class="docutils literal notranslate"><span class="pre">vocab_tree_matcher</span></code>, <code class="docutils literal notranslate"><span class="pre">sequential_matcher</span></code>,
<code class="docutils literal notranslate"><span class="pre">spatial_matcher</span></code>, <code class="docutils literal notranslate"><span class="pre">transitive_matcher</span></code>, <code class="docutils literal notranslate"><span class="pre">matches_importer</span></code>:
Perform feature matching after performing feature extraction.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">mapper</span></code>: Sparse 3D reconstruction / mapping of the dataset using SfM after
performing feature extraction and matching.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">hierarchical_mapper</span></code>: Sparse 3D reconstruction / mapping of the dataset
using hierarchical SfM after performing feature extraction and matching.
This parallelizes the reconstruction process by partitioning the scene into
overlapping submodels and then reconstructing each submodel independently.
Finally, the overlapping submodels are merged into a single reconstruction.
It is recommended to run a few rounds of point triangulation and bundle
adjustment after this step.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">image_undistorter</span></code>: Undistort images and/or export them for MVS or to
external dense reconstruction software, such as CMVS/PMVS.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">image_rectifier</span></code>: Stereo rectify cameras and undistort images for stereo
disparity estimation.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">image_filterer</span></code>: Filter images from a sparse reconstruction.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">image_deleter</span></code>: Delete specific images from a sparse reconstruction.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">patch_match_stereo</span></code>: Dense 3D reconstruction / mapping using MVS after
running the <code class="docutils literal notranslate"><span class="pre">image_undistorter</span></code> to initialize the workspace.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">stereo_fusion</span></code>: Fusion of <code class="docutils literal notranslate"><span class="pre">patch_match_stereo</span></code> results into to a colored
point cloud.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">poisson_mesher</span></code>: Meshing of the fused point cloud using Poisson
surface reconstruction.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">delaunay_mesher</span></code>: Meshing of the reconstructed sparse or dense point cloud
using a graph cut on the Delaunay triangulation and visibility voting.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">image_registrator</span></code>: Register new images in the database against an existing
model, e.g., when extracting features and matching newly added images in a
database after running <code class="docutils literal notranslate"><span class="pre">mapper</span></code>. Note that no bundle adjustment or
triangulation is performed.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">point_triangulator</span></code>: Triangulate all observations of registered images in
an existing model using the feature matches in a database.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">point_filtering</span></code>: Filter sparse points in model by enforcing criteria,
such as minimum track length, maximum reprojection error, etc.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">bundle_adjuster</span></code>: Run global bundle adjustment on a reconstructed scene,
e.g., when a refinement of the intrinsics is needed or
after running the <code class="docutils literal notranslate"><span class="pre">image_registrator</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">database_creator</span></code>: Create an empty COLMAP SQLite database with the
necessary database schema information.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">database_merger</span></code>: Merge two databases into a new database. Note that the
cameras will not be merged and that the unique camera and image identifiers
might change during the merging process.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">model_analyzer</span></code>: Print statistics about reconstructions.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">model_aligner</span></code>: Align/geo-register model to coordinate system of given
camera centers.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">model_orientation_aligner</span></code>: Align the coordinate axis of a model using a
Manhattan world assumption.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">model_converter</span></code>: Convert the COLMAP export format to another format,
such as PLY or NVM.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">model_cropper</span></code>: Crop model to specific bounding box described in GPS or
model coordinate system.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">model_splitter</span></code>: Divide model in rectangular sub-models specified from
file containing bounding box coordinates, or max extent of sub-model, or
number of subdivisions in each dimension.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">model_merger</span></code>: Attempt to merge two disconnected reconstructions,
if they have common registered images.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">color_extractor</span></code>: Extract mean colors for all 3D points of a model.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">vocab_tree_builder</span></code>: Create a vocabulary tree from a database with
extracted images. This is an offline procedure and can be run once, while the
same vocabulary tree can be reused for other datasets. Note that, as a rule of
thumb, you should use at least 10-100 times more features than visual words.
Pre-trained trees can be downloaded from <a class="reference external" href="https://demuc.de/colmap/">https://demuc.de/colmap/</a>.
This is useful if you want to build a custom tree with a different trade-off
in terms of precision/recall vs. speed.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">vocab_tree_retriever</span></code>: Perform vocabulary tree based image retrieval.</p></li>
</ul>
</section>
<section id="visualization">
<h2>Visualization<a class="headerlink" href="#visualization" title="Permalink to this heading"></a></h2>
<p>If you want to quickly visualize the outputs of the sparse or dense
reconstruction pipelines, COLMAP offers you the following possibilities:</p>
<ul class="simple">
<li><p>The sparse point cloud obtained with the <code class="docutils literal notranslate"><span class="pre">mapper</span></code> can be visualized via the
COLMAP GUI by importing the following files: choose <code class="docutils literal notranslate"><span class="pre">File</span> <span class="pre">&gt;</span> <span class="pre">Import</span> <span class="pre">Model</span></code>
and select the folder where the three files, <code class="docutils literal notranslate"><span class="pre">cameras.txt</span></code>, <code class="docutils literal notranslate"><span class="pre">images.txt</span></code>,
and <code class="docutils literal notranslate"><span class="pre">points3d.txt</span></code> are located.</p></li>
<li><p>The dense point cloud obtained with the <code class="docutils literal notranslate"><span class="pre">stereo_fusion</span></code> can be visualized
via the COLMAP GUI by importing <code class="docutils literal notranslate"><span class="pre">fused.ply</span></code>: choose
<code class="docutils literal notranslate"><span class="pre">File</span> <span class="pre">&gt;</span> <span class="pre">Import</span> <span class="pre">Model</span> <span class="pre">from...</span></code> and then select the file <code class="docutils literal notranslate"><span class="pre">fused.ply</span></code>.</p></li>
<li><p>The dense mesh model <code class="docutils literal notranslate"><span class="pre">meshed-*.ply</span></code> obtained with the <code class="docutils literal notranslate"><span class="pre">poisson_mesher</span></code> or
the <code class="docutils literal notranslate"><span class="pre">delaunay_mesher</span></code> can currently not be visualized with COLMAP, instead
you can use an external viewer, such as Meshlab.</p></li>
</ul>
</section>
</section>


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